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Moayedi Y, Rodenas-Alesina E, Somerset E, Fan CPS, Henricksen E, Aleksova N, Billia F, Chih S, Ross HJ, Teuteberg JJ. Enhancing the Prediction of Cardiac Allograft Vasculopathy Using Intravascular Ultrasound and Machine Learning: A Proof of Concept. Circ Heart Fail 2024; 17:e011306. [PMID: 38314558 DOI: 10.1161/circheartfailure.123.011306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024]
Abstract
BACKGROUND Cardiac allograft vasculopathy (CAV) is the leading cause of late graft dysfunction in heart transplantation. Building on previous unsupervised learning models, we sought to identify CAV clusters using serial maximal intimal thickness and baseline clinical risk factors to predict the development of early CAV. METHODS This is a single-center retrospective study including adult heart transplantation recipients. A latent class mixed-effects model was used to identify patient clusters with similar trajectories of maximal intimal thickness posttransplant and pretransplant covariates associated with each cluster. RESULTS Among 186 heart transplantation recipients, we identified 4 patient phenotypes: very low, low, moderate, and high risk. The 5-year risk (95% CI) of the International Society for Heart and Lung Transplantation-defined CAV in the high, moderate, low, and very low risk groups was 49.1% (35.2%-68.5%), 23.4% (13.3%-41.2%), 5.0% (1.3%-19.6%), and 0%, respectively. Only patients in the moderate to high risk cluster developed the International Society for Heart and Lung Transplantation CAV 2-3 at 5 years (P=0.02). Of the 4 groups, the low risk group had significantly younger female recipients, shorter ischemic time, and younger female donors compared with the high risk group. CONCLUSIONS We identified 4 clusters characterized by distinct maximal intimal thickness trajectories. These clusters were shown to discriminate against the development of angiographic CAV. This approach allows for the personalization of surveillance and CAV-directed treatment before the development of angiographically apparent disease.
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Affiliation(s)
- Yasbanoo Moayedi
- Ted Rogers Centre of Excellence in Heart Research (Y.M., E.R.-A., N.A., F.B., H.J.R.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada (Y.M., N.A., F.B., H.J.R.)
| | - Eduard Rodenas-Alesina
- Ted Rogers Centre of Excellence in Heart Research (Y.M., E.R.-A., N.A., F.B., H.J.R.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Emily Somerset
- Ted Rogers Computational Program, Centre of Excellence in Heart Function (E.S., C.P.S.F.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Chun Po S Fan
- Ted Rogers Computational Program, Centre of Excellence in Heart Function (E.S., C.P.S.F.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | | | - Natasha Aleksova
- Ted Rogers Centre of Excellence in Heart Research (Y.M., E.R.-A., N.A., F.B., H.J.R.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada (Y.M., N.A., F.B., H.J.R.)
| | - Filio Billia
- Ted Rogers Centre of Excellence in Heart Research (Y.M., E.R.-A., N.A., F.B., H.J.R.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada (Y.M., N.A., F.B., H.J.R.)
| | - Sharon Chih
- Ottawa Heart Institute, University of Ottawa, ON, Canada (S.C.)
| | - Heather J Ross
- Ted Rogers Centre of Excellence in Heart Research (Y.M., E.R.-A., N.A., F.B., H.J.R.), Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada (Y.M., N.A., F.B., H.J.R.)
| | - Jeffrey J Teuteberg
- Section of Heart Failure, Cardiac Transplant, and Mechanical Circulatory Support, Department of Medicine, Stanford University, CA (J.J.T.)
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Peyster EG, Janowczyk A, Swamidoss A, Kethireddy S, Feldman MD, Margulies KB. Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy. Circulation 2022; 145:1563-1577. [PMID: 35405081 DOI: 10.1161/circulationaha.121.058459] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. While clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high vs. low risk of developing aggressive CAV. The aim of this investigation was to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMB) to develop a precision medicine tool for predicting CAV years before overt clinical presentation. Methods: Clinical data from 1-year post-transplant was collected on 302 transplant recipients from the University of Pennsylvania, including 53 'early CAV' patients and 249 'no-CAV' controls. This data was used to generate a 'clinical model' (ClinCAV-Pr) for predicting future CAV development. From this cohort, n=183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year post-transplant EMBs from 50 'early CAV' patients and 82 no-CAV patients, as well as 51 EMBs from 'disease control' patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically-inspired, hand-crafted features extracted from digitized EMBs, quantitative histologic models for differentiating no-CAV from disease controls (HistoCAV-Dx), and for predicting future CAV from 1-year post-transplant EMBs were developed (HistoCAV-Pr). The performance of histologic and clinical models for predicting future CAV (i.e. HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set, before being combined to assess the added predictive value of an integrated predictive model (iCAV-Pr). Results: ClinCAV-Pr achieved modest performance on the independent test set, with area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, while HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set. Conclusions: Prediction of future CAV development is greatly improved by incorporation of computationally extracted histologic features. These results suggest morphologic details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for post-heart transplant patients.
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Affiliation(s)
- Eliot G Peyster
- Cardiovascular Research Institute (E.G.P., K.B.M.), University of Pennsylvania, Philadelphia
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
- Department of Oncology, Lausanne University Hospital and Lausanne University, Switzerland (A.J.)
| | - Abigail Swamidoss
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
| | - Samhith Kethireddy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (A.J., A.S., S.K.)
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine (M.D.F.), University of Pennsylvania, Philadelphia
| | - Kenneth B Margulies
- Cardiovascular Research Institute (E.G.P., K.B.M.), University of Pennsylvania, Philadelphia
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